AutoStreamPipe: LLM Assisted Automatic Generation of Data Stream Processing Pipelines

2025-10-27

AI Summary

To address low pipeline design efficiency, large semantic gaps, and high error rates in real-time data stream processing, this paper proposes the Hypergraph of Thoughts (HGoT) framework. HGoT leverages large language models (LLMs) to interpret high-level user intent and constructs a hypergraph-structured, multi-agent collaborative reasoning mechanism that enables end-to-end automated generation—from semantic understanding and logical modeling to cross-platform deployment and elastic optimization. Its core innovation lies in synergistically integrating LLM-based semantic comprehension with hypergraph-based symbolic reasoning to bridge the semantic gap between user intent and distributed system implementation. Additionally, HGoT introduces advanced query analysis and dynamic execution strategies to support automatic modeling and performance optimization of complex streaming logic. Experimental results demonstrate that, compared to conventional LLM-based code generation approaches, HGoT improves development efficiency by 6.3× and reduces error rates by 5.19×, significantly enhancing both generated code quality and system reliability.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Machine Learning: Time-Series/Data Streams
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Data pipelines are essential in stream processing as they enable the efficient collection, processing, and delivery of real-time data, supporting rapid data analysis. In this paper, we present AutoStreamPipe, a novel framework that employs Large Language Models (LLMs) to automate the design, generation, and deployment of stream processing pipelines. AutoStreamPipe bridges the semantic gap between high-level user intent and platform-specific implementations across distributed stream processing systems for structured multi-agent reasoning by integrating a Hypergraph of Thoughts (HGoT) as an extended version of GoT. AutoStreamPipe combines resilient execution strategies, advanced query analysis, and HGoT to deliver pipelines with good accuracy. Experimental evaluations on diverse pipelines demonstrate that AutoStreamPipe significantly reduces development time (x6.3) and error rates (x5.19), as measured by a novel Error-Free Score (EFS), compared to LLM code-generation methods.

Problem

• Automates design of stream processing pipelines using LLMs
• Bridges semantic gap between user intent and implementations
• Reduces development time and error rates significantly

Innovation

• LLMs automate stream pipeline design and deployment
• Hypergraph of Thoughts bridges semantic implementation gaps
• Combines resilient execution with advanced query analysis